# Python - Sets

Mathematically a set is a collection of items not in any particular order. A Python set is similar to this mathematical definition with below additional conditions.

• The elements in the set cannot be duplicates.
• The elements in the set are immutable(cannot be modified) but the set as a whole is mutable.
• There is no index attached to any element in a python set. So they do not support any indexing or slicing operation.

## Set Operations

The sets in python are typically used for mathematical operations like union, intersection, difference and complement etc. We can create a set, access it’s elements and carry out these mathematical operations as shown below.

## Creating a set

A set is created by using the set() function or placing all the elements within a pair of curly braces.

Days=set(["Mon","Tue","Wed","Thu","Fri","Sat","Sun"])
Months={"Jan","Feb","Mar"}
Dates={21,22,17}
print(Days)
print(Months)
print(Dates)

When the above code is executed, it produces the following result. Please note how the order of the elements has changed in the result.

set(['Wed', 'Sun', 'Fri', 'Tue', 'Mon', 'Thu', 'Sat'])
set(['Jan', 'Mar', 'Feb'])
set([17, 21, 22])

## Accessing Values in a Set

We cannot access individual values in a set. We can only access all the elements together as shown above. But we can also get a list of individual elements by looping through the set.

Days=set(["Mon","Tue","Wed","Thu","Fri","Sat","Sun"])

for d in Days:
print(d)

When the above code is executed, it produces the following result.

Wed
Sun
Fri
Tue
Mon
Thu
Sat

## Adding Items to a Set

We can add elements to a set by using add() method. Again as discussed there is no specific index attached to the newly added element.

Days=set(["Mon","Tue","Wed","Thu","Fri","Sat"])

print(Days)

When the above code is executed, it produces the following result.

set(['Wed', 'Sun', 'Fri', 'Tue', 'Mon', 'Thu', 'Sat'])

## Removing Item from a Set

We can remove elements from a set by using discard() method. Again as discussed there is no specific index attached to the newly added element.

Days=set(["Mon","Tue","Wed","Thu","Fri","Sat"])

print(Days)

When the above code is executed, it produces the following result.

set(['Wed', 'Fri', 'Tue', 'Mon', 'Thu', 'Sat'])

## Union of Sets

The union operation on two sets produces a new set containing all the distinct elements from both the sets. In the below example the element “Wed” is present in both the sets.

DaysA = set(["Mon","Tue","Wed"])
DaysB = set(["Wed","Thu","Fri","Sat","Sun"])
AllDays = DaysA|DaysB
print(AllDays)

When the above code is executed, it produces the following result. Please note the result has only one “wed”.

set(['Wed', 'Fri', 'Tue', 'Mon', 'Thu', 'Sat'])

## Intersection of Sets

The intersection operation on two sets produces a new set containing only the common elements from both the sets. In the below example the element “Wed” is present in both the sets.

DaysA = set(["Mon","Tue","Wed"])
DaysB = set(["Wed","Thu","Fri","Sat","Sun"])
AllDays = DaysA & DaysB
print(AllDays)

When the above code is executed, it produces the following result. Please note the result has only one “wed”.

set(['Wed'])

## Difference of Sets

The difference operation on two sets produces a new set containing only the elements from the first set and none from the second set. In the below example the element “Wed” is present in both the sets so it will not be found in the result set.

DaysA = set(["Mon","Tue","Wed"])
DaysB = set(["Wed","Thu","Fri","Sat","Sun"])
AllDays = DaysA - DaysB
print(AllDays)

When the above code is executed, it produces the following result. Please note the result has only one “wed”.

set(['Mon', 'Tue'])

## Compare Sets

We can check if a given set is a subset or superset of another set. The result is True or False depending on the elements present in the sets.

DaysA = set(["Mon","Tue","Wed"])
DaysB = set(["Mon","Tue","Wed","Thu","Fri","Sat","Sun"])
SubsetRes = DaysA <= DaysB
SupersetRes = DaysB >= DaysA
print(SubsetRes)
print(SupersetRes)

When the above code is executed, it produces the following result.

True
True

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Ans: Python Data Structure Introduction - Learn Python Data Structure in simple and easy steps starting from basic to advanced concepts with examples including Introduction, Environment, Arrays, Lists, Tuples, Dictionary, 2-D Array, Matrix, Sets, Maps, Linked Lists, Stack, Queue, Dequeue, Advanced Linked list, Hash Table, Binary Tree, Search Tree, Heaps, Graphs, Algorithm Design, Divide and conquer, Recursion, backtracking, Tree Traversal, Sorting, Searching, Graph Algorithms, Algorithm Analysis, Big-O Notation, Algorithim classes, Amortized analysis, Algorithm Justifications. view more..
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Ans: Python Data Structure Introduction - Learn Python Data Structure in simple and easy steps starting from basic to advanced concepts with examples including Introduction, Environment, Arrays, Lists, Tuples, Dictionary, 2-D Array, Matrix, Sets, Maps, Linked Lists, Stack, Queue, Dequeue, Advanced Linked list, Hash Table, Binary Tree, Search Tree, Heaps, Graphs, Algorithm Design, Divide and conquer, Recursion, backtracking, Tree Traversal, Sorting, Searching, Graph Algorithms, Algorithm Analysis, Big-O Notation, Algorithim classes, Amortized analysis, Algorithm Justifications. view more..
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Ans: Python Data Structure Introduction - Learn Python Data Structure in simple and easy steps starting from basic to advanced concepts with examples including Introduction, Environment, Arrays, Lists, Tuples, Dictionary, 2-D Array, Matrix, Sets, Maps, Linked Lists, Stack, Queue, Dequeue, Advanced Linked list, Hash Table, Binary Tree, Search Tree, Heaps, Graphs, Algorithm Design, Divide and conquer, Recursion, backtracking, Tree Traversal, Sorting, Searching, Graph Algorithms, Algorithm Analysis, Big-O Notation, Algorithim classes, Amortized analysis, Algorithm Justifications. view more..
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Ans: Python Data Structure Introduction - Learn Python Data Structure in simple and easy steps starting from basic to advanced concepts with examples including Introduction, Environment, Arrays, Lists, Tuples, Dictionary, 2-D Array, Matrix, Sets, Maps, Linked Lists, Stack, Queue, Dequeue, Advanced Linked list, Hash Table, Binary Tree, Search Tree, Heaps, Graphs, Algorithm Design, Divide and conquer, Recursion, backtracking, Tree Traversal, Sorting, Searching, Graph Algorithms, Algorithm Analysis, Big-O Notation, Algorithim classes, Amortized analysis, Algorithm Justifications. view more..
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Ans: Python Data Structure Introduction - Learn Python Data Structure in simple and easy steps starting from basic to advanced concepts with examples including Introduction, Environment, Arrays, Lists, Tuples, Dictionary, 2-D Array, Matrix, Sets, Maps, Linked Lists, Stack, Queue, Dequeue, Advanced Linked list, Hash Table, Binary Tree, Search Tree, Heaps, Graphs, Algorithm Design, Divide and conquer, Recursion, backtracking, Tree Traversal, Sorting, Searching, Graph Algorithms, Algorithm Analysis, Big-O Notation, Algorithim classes, Amortized analysis, Algorithm Justifications. view more..
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